Syed Bashir Hydari is a Graduate Coach with InGenius Prep and a Columbia-trained quantitative finance
researcher whose work sits at the intersection of mathematical uncertainty and capital decision systems.
He is the ideal coach for students targeting quantitative finance, statistics, applied mathematics, data
science, and economics programs at the most competitive institutions in the world—particularly those
who want to build the kind of technically rigorous, intellectually differentiated academic profile that elite
graduate programs and research-track careers are looking for.
Syed’s academic record reflects exceptional performance at both the undergraduate and graduate levels.
At UCLA, he graduated Summa Cum Laude on a full-ride merit scholarship with a perfect 4.0 STEM
GPA – ranking in the top 1% of his class – while earning his B.S. in Psychobiology (focused on
computational statistics), after completing a departmental thesis. He then earned his M.A. in Statistics
(focused on Applied Mathematics) from Columbia University on a competitive OPP-GSAS Full
Scholarship valued at $115,000, one of only three awarded by the department, graduating with a 4.066
GPA and specializing in quantitative risk and financial modeling. He was subsequently selected for
publication-track research under advisership of Dr. Mikhail Smirnov in Columbia University’s
Department of Mathematics, where he first-authored a study developing and evaluating a regime-adaptive
portfolio allocation framework through rigorous out-of-sample testing across multiple pre-registered
market universes and historical environments.
What distinguishes Syed is that he has not only studied quantitative finance at the highest academic level
– he has practiced it. As a Quant Risk Strategist at Risk Haas, he built a SQL-backed portfolio risk engine
for investment and family-office mandates, cutting shock-to-decision latency by roughly 40% during
market stress. At Delta Strategy Co., he originated a proprietary quantitative portfolio research framework
inside a senior buy-side initiative, developing it under live market conditions through strict out-of-sample
methodology and simulation-based stress testing across multiple market regimes. He also ran a semi-
systematic equity trading system against his own live capital for years before formalizing its broader
conceptual foundations in a practitioner book published by B&N Press in 2022. His research methods in
quantitative finance trace back to a post-baccalaureate fellowship at UCLA, where he developed Bayesian
and stochastic inference models on neural time-series data under Dr. Arisaka – methods he would later
adapt to quantitative financial decision-making under uncertainty.
As a Graduate Coach, Syed works with students at the intersection of mathematics, statistics, and
quantitative sciences, helping them develop the technical depth, research clarity, and application strategy
that the most competitive STEM and finance programs demand. His own trajectory – from a perfect
STEM GPA on a full scholarship to a full-tuition Columbia fellowship to first-authored publication-stage
research to real buy-side practice – is a blueprint for what an exceptional quantitative profile looks like,
and one he helps ambitious students begin building from wherever they are.
